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imm_smooth

PURPOSE ^

IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.

SYNOPSIS ^

function [x_sk,P_sk,x_sik,P_sik,mu_sk] = imm_smooth(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)

DESCRIPTION ^

IMM_SMOOTH   Fixed-interval IMM smoother using two IMM-filters.

 Syntax:
   [X_S,P_S,X_IS,P_IS,MU_S] = IMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)

 In:
   MM    - NxK matrix containing the means of forward-time 
           IMM-filter on each time step
   PP    - NxNxK matrix containing the covariances of forward-time
           IMM-filter on each time step
   MM_i  - Model-conditional means of forward-time IMM-filter on each time step
           as a cell array
   PP_i  - Model-conditional covariances of forward-time IMM-filter on each time
           step as a cell array
   MU    - Model probabilities of forward-time IMM-filter on each time step 
   p_ij  - Model transition probability matrix
   mu_0j - Prior model probabilities
   ind   - Indices of state components for each model as a cell array
   dims  - Total number of different state components in the combined system
   A     - State transition matrices for each model as a cell array.
   Q     - Process noise matrices for each model as a cell array.
   R     - Measurement noise matrices for each model as a cell array.
   H     - Measurement matrices for each model as a cell array
   Y     - Measurement sequence


 Out:
   X_S  - Smoothed state means for each time step
   P_S  - Smoothed state covariances for each time step
   X_IS - Model-conditioned smoothed state means for each time step
   P_IS - Model-conditioned smoothed state covariances for each time step
   MU_S - Smoothed model probabilities for each time step
   
 Description:
   Two filter fixed-interval IMM smoother.

 See also:
   IMM_UPDATE, IMM_PREDICTION, IMM_FILTER

CROSS-REFERENCE INFORMATION ^

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